The SPE Foundation through member donations and a ... · Imaging and modelling of flooding at...
Transcript of The SPE Foundation through member donations and a ... · Imaging and modelling of flooding at...
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Primary funding is provided by
The SPE Foundation through member donations
and a contribution from Offshore Europe
The Society is grateful to those companies that allow their
professionals to serve as lecturers
Additional support provided by AIME
Society of Petroleum Engineers
Distinguished Lecturer Program www.spe.org/dl
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From 3D pore scale imaging to Reservoir
inputs: Next generation reservoir
characterization & description
Society of Petroleum Engineers
Distinguished Lecturer Program www.spe.org/dl 2
Mark Knackstedt [email protected]
Oil and Gas
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Overview
• Problem Statement
– The challenge of integrating scale
• Digital rock technology
– Introduction: Pore to plug
– Fast turnaround & Sensitivity Studies
– Enhanced Understanding
• Multiscale Reservoir Characterization
– Upscaling and Uncertainties
– Upscaling in a SYSTEMATIC manner (calibrated at each
scale)
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Problem Statement Size matters in reservoir characterization
Reservoir model
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The Multi-scale Reservoir Characterisation
Problem
?
Question:
How do we accurately and effectively model fine-scale
heterogeneities & predict petrophysical response & fluid flow
at larger scales (log/geo/reservoir-scale)
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Properties are measured at a range of scales
Goal: Assign properties measured at fine scale to the commercial scales.
Spatial Resolution
(m) 10-9 10-6 10-5 10-3 10-2
Nanopore Pore Plug Whole Core Well Log Geomodel Reservoir
model
10-1 1 10 102
Physics & data changes 6
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Multi-scale Reservoir Characterisation
x 103
x 102
x 104
x 104 1D
0D
Challenge: We need to assign properties measured at fine scale
to the commercial scales
2D
Dynamic Model
Static Model
Well Log
Core Plug
250 m x 250 m
x 4 m
60 m x 60 m
x 0.6 m
0.6 m x 0.6 m
x 0.6 m
0.025 m x 0.025 m
x 0.038m
x 100
x 10 000
x 10 000
Dynamic Model
Static Model
Well Log
Core Plug
250 m x 250 m
x 4 m
60 m x 60 m
x 0.6 m
0.6 m x 0.6 m
x 0.6 m
0.025 m x 0.025 m
x 0.038m
x 100
x 10 000
x 10 000
MicroCT PlugMicroCT Plug
x 1 000
2.5 mm x 2.5 mm
x 3.7 mm
2.5 cm x 2.5 cm
x 3.7 cm
0.6 m x 0.6 m
x 0.64 m
50 m x 50 m
x 0.93 m
250 m x 250 m
x 3.7 m
MicroCT PlugMicroCT Plug
x 1 000
0.0025 m x 0.0025 m
x 0.0037 m
0.025 m x 0.025 m
x 0.037 m
0.6 m x 0.6 m
x 0.64 m
50 m x 50 m
x 0.93 m
250 m x 250 m
x 3.7 m
Dynamic Model
Static Model
Well Log
Core Plug
250 m x 250 m
x 4 m
60 m x 60 m
x 0.6 m
0.6 m x 0.6 m
x 0.6 m
0.025 m x 0.025 m
x 0.038m
x 100
x 10 000
x 10 000
Dynamic Model
Static Model
Well Log
Core Plug
250 m x 250 m
x 4 m
60 m x 60 m
x 0.6 m
0.6 m x 0.6 m
x 0.6 m
0.025 m x 0.025 m
x 0.038m
x 100
x 10 000
x 10 000
MicroCT PlugMicroCT Plug
x 1 000
0.0025 m x 0.0025 m
x 0.0037 m
0.025 m x 0.025 m
x 0.037 m
0.6 m x 0.6 m
x 0.64 m
50 m x 50 m
x 0.93 m
250 m x 250 m
x 3.7 m
Pore
3D
3D
+
+
+
3D
3D
3D
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Overview • Problem Statement
– The challenge of integrating scale
• Digital rock technology: Pore to Plug
– Technology Development and alternate SCAL data
– Enhanced Understanding
8 Spatial
Resolution (m) 10-9 10-6 10-5 10-3 10-2
Nanopore Pore Plug Whole Core Well Log Geomodel Reservoir
model
10-1 1 10 102
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Digital Rock Technology
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1
10
100
1000
10000
0,0 0,1 0,2 0,3
Porosity
[fraction]
Per
mea
bil
ity
[mD
]
Elastic moduli
FRF
Porosity
NMR relaxation times
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Sw
Rel P
erm
krw_exp1kro_exp1krw_exp2kro_exp2krw_exp3
kro_exp3krw simkro simkrw avgkro avg
Rel
ati
ve p
erm
eab
ilit
y
1.0
0.8
0.6
0.4
0.2
0.0 1.0 0.8 0.6 0.4 0.2 0.0
Sw [fraction]
Relative permeability
Capillary pressure
Resistivity index
PETROPHYSICAL FLUID FLOW
DIGITAL ROCK PROPERTIES
Absolute permeability
3D DIGITAL ROCK
Cementation exponent
Acoustic velocities
Saturation exponent
Mercury injection
Sw sensitivity
Wettability analysis
IFT sensitivity
Rate sensitivity
EOR/IOR
Oil in Place
Formation damage
4D IMAGING
Fluid sensitivity
Geochem. Reactivity
Unconventional Play
QUALITATIVE
Wettability mapping
Digital Workflow
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Workflow – Conventional
Normalised Data vs Pore Throat Size
0.0
0.2
0.4
0.6
0.8
1.0
0.001 0.01 0.1 1 10 100 1000Pore Throat Radius (Microns)
Dis
trib
uti
on
Fu
ncti
on
s
Lab MICP
DRP MICP
0.0
0.2
0.4
0.6
0.8
1.0
0.1 1 10 100 1000 10000
T2 [ms]
No
rma
lis
ed
Am
plitu
de
Sample 6 Exp
Sample 6 DRP
BB 274 88 RT3
-120
-80
-40
0
40
80
120
0.00 0.20 0.40 0.60 0.80 1.00
Water saturation, Sw (frac.)
Cap
illa
ry p
ress
ure
, P
c (p
si) Pc Exp
Hg curvePc SimPc Exp SIPc Exp FIPc Sim
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
0.0 0.2 0.4 0.6 0.8 1.0Sw
Re
lative
pe
rme
ab
ility
DRP - CC.RRT6
Lab - CC.RRT6
HM - Lab - CC.RRT6
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1) SCAL: Fast turnaround, Difficult cases
Derive (primarily) kr and Pc data for reservoir modelling with
fast turnaround. Undertake otherwise impossible SCAL:
Cuttings, Sidewall, damaged core.
2) Enhanced Understanding
Fluid distributions, EOR/IOR recovery mechanisms, wettability,
heterogeneity, uncertainties
3) Upscaling and Uncertainties
Upscaling in a SYSTEMATIC manner (calibrating at each scale).
Analyse heterogeneous/complex rocks (e.g, thin beds,
carbonates)
Main Application Areas for Digital Rock
Technology
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1) SCAL: Fast turnaround, Difficult cases
Derive (primarily) kr and Pc data for reservoir modelling with
fast turnaround
• 2-3 month turnaround
• Multiple sensitivity studies: Incorporate ranges of
behaviour– perform 100’s of experiments on same
core plug to understand options and uncertainties
• Next generation reservoir description: Orders of
magnitude more data (SPE Forum 2011)
• Applicable on sidewall and cuttings. Restoration of
data for old core material
Main Application Areas for Digital Rock Analysis
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Project Example: ADCO Carbonate Reservoir Study
• Within <1 year, 95 core plugs were analysed:
• A data set of porosity, permeability, FRF, m, n
• Samples represent 16 rock types spanning 4 orders of magnitude in permeability and 20 porosity units
• Pc curves for primary drainage and water flooding
• kr curves for primary drainage, water flooding and gas/oil @ Swi
• Resistivity index curves for primary drainage and water flooding
• Two Papers at 2012 Society of Core Analysts: SCA2012-03 & 13
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Predicted vs. Experimental
Porosity [frac.]
0.00
0.10
0.20
0.30
0.40
0.50
0.00 0.10 0.20 0.30 0.40 0.50experimental
DR
P
Fm1-Field1
Fm2-Field1
Fm2-Field2
Fm2-Field3
1:1
range 4 por units
Permeability in mD
1.E-01
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04experimental
DR
P
Fm1-Field1
Fm2-Field1
Fm2-Field2
Fm2-Field3
1:1
Factor 2 range
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Example: NMR - Pc - MICP
0.0
0.2
0.4
0.6
0.8
1.0
0.1 1 10 100 1000 10000
T2 [ms]
No
rma
lis
ed
Am
plitu
de
Sample 6 Exp
Sample 6 DRP
Normalised Data vs Pore Throat Size
0.0
0.2
0.4
0.6
0.8
1.0
0.001 0.01 0.1 1 10 100 1000Pore Throat Radius (Microns)
Dis
trib
uti
on
Fu
ncti
on
s
Lab MICP
DRP MICP
Saturation vs Pore Throat Size
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.001 0.01 0.1 1 10 100Pore Throat Radius (Microns)
Mercu
ry
Sa
tura
tio
n (
Fra
c)
Lab MICP
DRP MICP
0.0
0.2
0.4
0.6
0.8
1.0
0.1 1 10 100 1000 10000
T2 [ms]
No
rma
lis
ed
Am
plitu
de
Sample 6 Exp
Sample 6 DRP
-120
-80
-40
0
40
80
0.00 0.20 0.40 0.60 0.80 1.00
Water saturation, Sw (frac.)
Cap
illa
ry p
ress
ure
, P
c (
psi
)
PcPD_Exp
Pc_DY_18_48_Sim
BB 274 88 RT3
-120
-80
-40
0
40
80
120
0.00 0.20 0.40 0.60 0.80 1.00
Water saturation, Sw (frac.)
Cap
illa
ry p
ress
ure
, P
c (
psi
) Pc ExpHg curvePc SimPc Exp SIPc Exp FIPc Sim
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R
R
T
6 0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0Sw
Re
lative
pe
rme
ab
ility
DRP - CC.RRT6
Lab - CC.RRT6
HM - Lab - CC.RRT6
Samlple ID RRT6 BU_570_36 BU_570_37 BU_570_106 DRP - CC.RRT6
k(mD) 17.5 13.3 25.8 9.31 23.1
Porosity (frac.) 0.275 0.26 0.301 0.264 0.269
Swi 0.134 0.1 0.06 0.11 0.09
Sorw 0.19 0.13 0.09 0.13 0.20
krw(Sorw) 0.62 0.88 0.86 0.82 0.70
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
0.0 0.2 0.4 0.6 0.8 1.0Sw
Re
lative
pe
rme
ab
ility
DRP - CC.RRT6
Lab - CC.RRT6
HM - Lab - CC.RRT6
Predicted vs. Experimental – SS Relative
Permeability
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Pore to Plug Bonus:
Understanding of processes
– Enhanced Understanding
Fluid distributions, EOR/IOR recovery
mechanisms, wettability,
heterogeneity, uncertainties
Enhanced Oil Recovery
Oil in Place
Formation damage
4D IMAGING
Fluid sensitivity
Geochemical reactivity
Unconventional reservoirs
QUALITATIVE
Wettability
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Pore scale wettability mapping
1
4
2
5
3
A1
A2
S w
Pc
0
+
-
10 S 1
S 3
S 5 S 2
S 4
Amott-Harvey Index
13
12
SS
SSIw
53
43
SS
SSI o
owAH III
USBM Index
2
1log
A
AIUSBM
Montaron, SPE 105041
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Wettability Analysis
• Wettability characterization at
molecular and pore scales
• Couple with direct imaging of fluid
distributions
Fogden, SCA 2011 21
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ENABLES FLUID MAPPING IN 3D
Dodd et al., IPTC 17696, 2014 22
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Multistage Experimental Flooding Workflow
• Sample preparation:
17mm
• Dry image: Axial pressure: 1600psi, confining pressure:
~1500psi, temperature: ~50
• Brine saturation: ~150 pore volume of brine passes
through the core (1.5mol KCl+1mol NaI), @0.01cc/min pore
pressure:~1300psi
• CO2 injection: ~50 pore volume of scCO2 @0.01cc/min is
flushed through the sample & pressure:~1300psi
• Brine injection: ~150 pore volume of brine @0.01cc/min is
flushe through the sample, pressure: ~1300psi
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Imaging of flooding at Reservoir T & P
X-Slices
Dry Brine 1 scCO2 Brine 2
( ) scCO2
( ) Brine
Legend
Residual scCO2
saturation: 25%
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Imaging and modelling of flooding
at Reservoir T & P
Comparison of Simulation with Time-Series Imaging
Sim
ula
tion
Tim
e S
eries
Imagin
g
Pore Network Drainage (to Sw=0.5 )
Imbibition
(Sco2 =29%) Segmented File
Time Series Imaging
Grain
Clay
Pore
scCO2/Air
Brine
Simulation
Grain
Clay
Pore
scCO2 Invasion
Trapped Brine
Trapped scCO2
Brine Imbibition
Brine 1 scCO2 Brine 2: SCO2=31% Segmented File
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• Upscaling & Uncertainties
– Upscaling in a SYSTEMATIC manner (calibrated at each
scale)
26
26 Spatial
Resolution (m) 10-9 10-6 10-5 10-3 10-2
Nanopore Pore Plug Whole Core Well Log Geomodel Reservoir
model
10-1 1 10 102
Moving beyond plug scale… new territory
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Basic Methodology
Ringrose, Svalbard Workshop: Modelling and risk assessment of geological
storage of CO2, 2009
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Pore to Whole core (Log scale) upscaling
via digital rock technology
• Imaging and registration at whole core down to pore scale enables one to to classify
(cluster) the rock at each scale into rock types
• Rock types can be treated as distinct units in the upscaling process
• Enables pore to plug, plug to core and core to log workflows that integrate and
upscale data in a consistent manner for improved reservoir characterisation
• Aim to deliver dynamic data anchored to log responses 28
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Images acquired and integrated at various scales
29
PU8
Section or whole
core image
oriented to show
registered plug
location
Core Scan
Plug Scan Mini-Plug Scan BSEM or FIBSEM
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Whole Core CT Original Image resolution = 200x200x2000 µm
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Porosity permeability trends by rock type
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0.1
1
10
100
1000
0.05 0.1 0.15 0.2 0.25 0.3 0.35
Porosity
Pe
rm (
mD
)
Rock 1
Rock2
Rock3
From plug images derive….
…properties derived on individual “cm-inch” scales (on individual rock types)
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Methodology that
honours
Properties and
Uncertainties at
EVERY Scale
Can Derive Effective
Upscaled Properties of
Complex (Laminated,
Carbonate) Intervals!
Drainage Waterflood
0
0,2
0,4
0,6
0,8
1
0 0,2 0,4 0,6 0,8 1
Sw
Re
l P
erm
0
1
2
3
4
5
0 0,2 0,4 0,6 0,8 1
Sw
Pc
(b
ar)
-5
-4
-3
-2
-1
0
1
2
0 0,2 0,4 0,6 0,8 1
Sw
Pc
(b
ar)
0
0,2
0,4
0,6
0,8
1
0 0,2 0,4 0,6 0,8 1
Sw
Re
l P
erm
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Static Properties to lithofacies scale
Vary geometry & properties (e.g., rock type
distribution, lamina thickness, permeability,
porosity
Propagate the variability identified at the
pore scale directly to lithofacies curves
Enumerate the most probable flow
properties at the lithofacies scale including
variability Rustad, SPE11305, 2008
Have identified rock types at pore scale
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Dynamic Properties to lithofacies scale
Enumerate the most probable MP flow properties at
the lithofacies scale including variability
Account for the direction of flow and the balance of
gravitational, viscous and capillary forces at various
scale: Need CL, VL & VE, tensorial based
Applied to full field gave significantly improved history
match
Klov, SPE84549, 2005
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Future: Multistage Process
Pore to core to log/facies to…. 36
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Conclusion: The Multi-scale Reservoir
Characterisation Problem
Question: How do we accurately and effectively model fine-scale heterogeneities that will
impact on petrophysical response & fluid flow in a reservoir-scale model?
? ?
Option: Follow a detailed, sequential, multi-scale, pore-to-plug-to-log- (to-
reservoir) characterisation workflow which incorporates 3D multiscale imaging,
robust classification and upscaling procedures 37
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Society of Petroleum Engineers
Distinguished Lecturer Program www.spe.org/dl 38
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